class Neuron(object):

  # ... 

  def forward(self, inputs):

    """ assume inputs and weights are 1-D numpy arrays and bias is a number """

    cell_body_sum = np.sum(inputs * self.weights) + self.bias

    firing_rate = 1.0 / (1.0 + math.exp(-cell_body_sum)) # sigmoid activation function

    return firing_rate

class Neuron(object):

  # ... 

  def forward(self, inputs):

    """ assume inputs and weights are 1-D numpy arrays and bias is a number """

    cell_body_sum = np.sum(inputs * self.weights) + self.bias

    firing_rate = 1.0 / (1.0 + math.exp(-cell_body_sum)) # sigmoid activation function

    return firing_rate

class Neuron(object):

  # ... 

  def forward(self, inputs):

    """ assume inputs and weights are 1-D numpy arrays and bias is a number """

    cell_body_sum = np.sum(inputs * self.weights) + self.bias

    firing_rate = 1.0 / (1.0 + math.exp(-cell_body_sum)) # sigmoid activation function

    return firing_rate

class Neuron(object):

  # ... 

  def forward(self, inputs):

    """ assume inputs and weights are 1-D numpy arrays and bias is a number """

    cell_body_sum = np.sum(inputs * self.weights) + self.bias

    firing_rate = 1.0 / (1.0 + math.exp(-cell_body_sum)) # sigmoid activation function

    return firing_rate

class Neuron(object):

  # ... 

  def forward(self, inputs):

    """ assume inputs and weights are 1-D numpy arrays and bias is a number """

    cell_body_sum = np.sum(inputs * self.weights) + self.bias

    firing_rate = 1.0 / (1.0 + math.exp(-cell_body_sum)) # sigmoid activation function

    return firing_rate

# forward-pass of a 3-layer neural network:

f = lambda x: 1.0/(1.0 + np.exp(-x)) # activation function (use sigmoid)

x = np.random.randn(3, 1) # random input vector of three numbers (3x1)

h1 = f(np.dot(W1, x) + b1) # calculate first hidden layer activations (4x1)

h2 = f(np.dot(W2, h1) + b2) # calculate second hidden layer activations (4x1)

out = np.dot(W3, h2) + b3 # output neuron (1x1)